Mastering Software Development in R Specialization Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This specialization provides a structured, beginner-friendly path to mastering software development in R, ideal for creating reusable data science tools. Over approximately 2 months with a commitment of about 10 hours per week, learners progress from foundational R programming to building and distributing packages and custom visualizations. The curriculum emphasizes hands-on practice, culminating in a capstone project using real-world data.
Module 1: The R Programming Environment
Estimated time: 3 hours
- R basics
- Tidy data concepts
- Data import and manipulation
- Text processing, memory, and large datasets
Module 2: Advanced R Programming
Estimated time: 40 hours
- Functional programming in R
- Debugging techniques
- Profiling and performance optimization
- Object-oriented design in R
Module 3: Building R Packages
Estimated time: 20 hours
- Package structure and organization
- Documentation and testing
- Licensing and version control
- Continuous integration and cross-platform development
Module 4: Building Data Visualization Tools
Estimated time: 40 hours
- Creating visualizations in R
- Interactive mapping
- Grid graphics system
- Designing custom graphical elements
Module 5: Mastering Software Development in R Capstone
Estimated time: 3 hours
- Data cleaning with NOAA Significant Earthquakes dataset
- Building custom geoms and mapping functions
- Documentation and deployment of software package
Module 6: Final Project
Estimated time: 3 hours
- Develop a complete R software package
- Create custom data visualization tools
- Submit documented package for review
Prerequisites
- No prior R experience required
- Basic computer literacy
- Interest in data science software development
What You'll Be Able to Do After
- Design and implement efficient R functions using functional and object-oriented programming
- Build, test, and distribute reusable R packages
- Create custom data visualizations and interactive maps
- Apply best practices in documentation, version control, and continuous integration
- Develop and deploy production-ready data science tools for real-world use